To solve the first type of problem, the early methods mainly concentrate on imputation techniques or deep generative approaches for data completion, such as matrix completion via matrix factorization [1]}, [2]}, [3]}, [4]} and generative adversarial learning [5]}. Finding that the imputation-based methods disconnect the processes of imputation and network learning, which decreases the diversity and discriminability of the learned representations, some advanced algorithms, e.g., GRAPE [6]} and GCNMF [7]}, merge the both processes of data imputation and representation learning into a united graph-based framework, where they adopt bipartite message passing strategy and Gaussian mixture model (GMM) to restore incomplete values, respectively. The above-mentioned methods could work well when handling attribute-incomplete problems. Nevertheless, they could hard to produce high-quality data completion when node attributes are entirely missing.
